GA
[1] Initialization: generate a population of \(S=S_e+S_m+S_p\)  individuals and the \(i\) -th individual has a randomly generated chromosome \(\mathbf {\Theta }_i\) ; the iteration
number \(q\)  = 1;
\(q \le N*100\) 
Fitness evaluation: Calculate the fitness of each individual by using the objective function in (REF ) or () and sort them in a descending order;
Selection: Based on the descending order, select the top \(S_e\)  individuals as elites;
Mutation: Create \(S_m\)  offspring from the last \(S_m\)  individuals by using uniform mutation [1]};
Crossover: Use stochastic universal sampling [1]} to generate \(2S_p\)  parents from the remaining \(S_p\)  individuals. Then use two-points crossover [1]} to create \(S_p\)  offspring from \(2S_p\)  parents;
Combine \(S_e\)  elites, \(S_m+S_p\)  offspring to form the next generation population; \(q=q+1\) ;
Output the chromosome of the most fit individual in the current population.
